Crossvalidation ensures the generalization of models from "summary" of Machine Learning by Ethem Alpaydin
Crossvalidation is a technique used to ensure the generalization of models. When we build a model using a training set and then evaluate it using a test set, we are assuming that the test set is representative of the population. However, in practice, this may not always be the case. The test set may be too small, leading to high variance in the performance of the model. Crossvalidation addresses this issue by partitioning the data into multiple subsets and using each subset as both a training set and a test set. By averaging the performance of the model over multiple iterations, we can get a more accurate estimate of how well the model will perform on unseen data. One common method of crossvalidation is k-fold crossvalidation, where the data is divided into k subsets. The model is trained on k-1 subsets and tested on the remaining subset. This process is repeated k times, with each subset used as the test set exactly once. Crossvalidation helps to prevent overfitting by providing a more robust estimate of the model's performance on unseen data. It also allows us to tune hyperparameters more effectively, as we can evaluate the model's performance on multiple subsets of the data. In summary, crossvalidation is a crucial tool in machine learning for ensuring the generalization of models. By testing the model on multiple subsets of the data, we can get a more accurate estimate of its performance on unseen data.
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